Showing papers on "Genetic algorithm published in 2020"
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Abstract: Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.
446 citations
TL;DR: To perform parameter optimization and feature selection simultaneously for SVM, an improved whale optimization algorithm (CMWOA), which combines chaotic and multi-swarm strategies is proposed, which significantly outperformed all the other competitors in terms of classification performance and feature subset size.
362 citations
TL;DR: This paper analyzes and summarizes the optimization effect of genetic algorithm in various energy management strategies, aiming to analyze and select the optimization rules and parameters, optimization objects and optimization objectives.
302 citations
TL;DR: Experimental results on a series of public datasets show that the effective combination of the binary mutation and OPS makes the MOFS-BDE achieve a trade-off between local exploitation and global exploration.
262 citations
TL;DR: The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm
248 citations
TL;DR: An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode and demonstrates that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets.
Abstract: Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets. The wrapper feature selection mode can be used in different application areas of expert and intelligent systems and this is confirmed from the obtained results over different types of datasets.
224 citations
TL;DR: The main focus of this paper is on the family of evolutionary algorithms and their real-life applications, and each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language.
Abstract: The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.
207 citations
TL;DR: The objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
Abstract: Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things ( IIOT ) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
153 citations
TL;DR: A self-learning genetic algorithm (SLGA) is proposed in this paper, in which genetic algorithm [GA] is adopted as the basic optimization method and its key parameters are intelligently adjusted based on reinforcement learning (RL).
149 citations
TL;DR: This work proposes a general strategy based on the combination between a recurrent neutral network method, i.e., long short-term memory, and hybrid genetic algorithm, and applies this predication method to supercapacitors charging at steady state mode and succeeds in estimating their remaining useful life.
125 citations
TL;DR: Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs and the effect of adaptive crossover and mutation probabilities on the optimal solution.
Abstract: In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.
TL;DR: Metaheuristic-search-based algorithms are used, known by their ability to alleviate search complexity as well as their capacity to learn from the domain where they are applied, to find optimal or near-optimal values for the set of tunable LSTM hyperparameters in the electrical energy consumption domain.
Abstract: Short term electric load forecasting plays a crucial role for utility companies, as it allows for the efficient operation and management of power grid networks, optimal balancing between production and demand, as well as reduced production costs. As the volume and variety of energy data provided by building automation systems, smart meters, and other sources are continuously increasing, long short-term memory (LSTM) deep learning models have become an attractive approach for energy load forecasting. These models are characterized by their capabilities of learning long-term dependencies in collected electric data, which lead to accurate prediction results that outperform several alternative statistical and machine learning approaches. Unfortunately, applying LSTM models may not produce acceptable forecasting results, not only because of the noisy electric data but also due to the naive selection of its hyperparameter values. Therefore, an optimal configuration of an LSTM model is necessary to describe the electric consumption patterns and discover the time-series dynamics in the energy domain. Finding such an optimal configuration is, on the one hand, a combinatorial problem where selection is done from a very large space of choices; on the other hand, it is a learning problem where the hyperparameters should reflect the energy consumption domain knowledge, such as the influential time lags, seasonality, periodicity, and other temporal attributes. To handle this problem, we use in this paper metaheuristic-search-based algorithms, known by their ability to alleviate search complexity as well as their capacity to learn from the domain where they are applied, to find optimal or near-optimal values for the set of tunable LSTM hyperparameters in the electrical energy consumption domain. We tailor both a genetic algorithm (GA) and particle swarm optimization (PSO) to learn hyperparameters for load forecasting in the context of energy consumption of big data. The statistical analysis of the obtained result shows that the multi-sequence deep learning model tuned by the metaheuristic search algorithms provides more accurate results than the benchmark machine learning models and the LSTM model whose inputs and hyperparameters were established through limited experience and a discounted number of experimentations.
TL;DR: The results show that the proposed self-adaptive discrete particle swarm optimization algorithm using the genetic algorithm (GA) operators can effectively reduce the system cost of off-loading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.
Abstract: Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge, and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this article, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators is proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers off-loading over the cloud, edge, and end devices. The mutation operator and crossover operator of GA are adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed off-loading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the system cost of off-loading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.
TL;DR: The results of computations showed that the trained MLP model proposed in this study can accurately predict energy consumption in the building and may be applied to similar buildings to predict and optimize energy consumption.
TL;DR: A novel algorithm named MGGS (modified genetic algorithm (GA) combined with greedy strategy) is proposed that can find an optimal solution using fewer number of iterations to optimize task scheduling process.
Abstract: Cloud computing is an emerging distributed system that provides flexible and dynamically scalable computing resources for use at low cost. Task scheduling in cloud computing environment is one of the main problems that need to be addressed in order to improve system performance and increase cloud consumer satisfaction. Although there are many task scheduling algorithms, existing approaches mainly focus on minimizing the total completion time while ignoring workload balancing. Moreover, managing the quality of service (QoS) of the existing approaches still needs to be improved. In this paper, we propose a novel algorithm named MGGS (modified genetic algorithm (GA) combined with greedy strategy). The proposed algorithm leverages the modified GA algorithm combined with greedy strategy to optimize task scheduling process. Different from existing algorithms, MGGS can find an optimal solution using fewer number of iterations. To evaluate the performance of MGGS, we compared the performance of the proposed algorithm with several existing algorithms based on the total completion time, average response time, and QoS parameters. The results obtained from the experiments show that MGGS performs well as compared to other task scheduling algorithms.
TL;DR: Experimental analysis shows that the ACO-FCP ensemble model is superior and more robust than its counterparts, and this study strongly recommends that the proposed ACO -FCP model is highly competitive than traditional and other artificial intelligence techniques.
TL;DR: It is shown how backup PV/battery systems can reduce electricity bills, even in countries where their electricity is cheap and subsidized.
TL;DR: It is concluded that the solution techniques can yield high-quality solutions and NSGA-II is considered as the most efficient solution tool, the optimal route planning of the case study problem in delivery and pick-up phases is attained using the best-found Pareto solution and the highest change in the objective function occurs for the total cost value by applying a 20% increase in the demand parameter.
TL;DR: A UAV distributed flocking control algorithm based on the modified MPIO is proposed to coordinate UAVs to fly in a stable formation under complex environments and comparison experiments are carried out to show the feasibility, validity, and superiority of the proposed algorithm.
TL;DR: The correctness, stability, and potential of the proposed FF-ANN-GASQP scheme for the four different cases are established through comparative assessment study from the results of numerical computing with Adams solver for single as well as multiple autonomous trials.
Abstract: The present study aims to design stochastic intelligent computational heuristics for the numerical treatment of a nonlinear SITR system representing the dynamics of novel coronavirus disease 2019 (COVID-19). The mathematical SITR system using fractal parameters for COVID-19 dynamics is divided into four classes; that is, susceptible (S), infected (I), treatment (T), and recovered (R). The comprehensive details of each class along with the explanation of every parameter are provided, and the dynamics of novel COVID-19 are represented by calculating the solution of the mathematical SITR system using feed-forward artificial neural networks (FF-ANNs) trained with global search genetic algorithms (GAs) and speedy fine tuning by sequential quadratic programming (SQP)—that is, an FF-ANN-GASQP scheme. In the proposed FF-ANN-GASQP method, the objective function is formulated in the mean squared error sense using the approximate differential mapping of FF-ANNs for the SITR model, and learning of the networks is proficiently conducted with the integrated capabilities of GA and SQP. The correctness, stability, and potential of the proposed FF-ANN-GASQP scheme for the four different cases are established through comparative assessment study from the results of numerical computing with Adams solver for single as well as multiple autonomous trials. The results of statistical evaluations further authenticate the convergence and prospective accuracy of the FF-ANN-GASQP method.
TL;DR: The proposed BPR system, based on statistical dependencies between behaviors and respective signal data, has been used to extract statistical features along with acoustic signal features like zero crossing rate to maximize the possibility of getting optimal feature values.
Abstract: Human behavior pattern recognition (BPR) from accelerometer signals is a challenging problem due to variations in signal durations of different behaviors. Analysis of human behaviors provides in depth observations of subject’s routines, energy consumption and muscular stress. Such observations hold key importance for the athletes and physically ailing humans, who are highly sensitive to even minor injuries. A novel idea having variant of genetic algorithm is proposed in this paper to solve complex feature selection and classification problems using sensor data. The proposed BPR system, based on statistical dependencies between behaviors and respective signal data, has been used to extract statistical features along with acoustic signal features like zero crossing rate to maximize the possibility of getting optimal feature values. Then, reweighting of features is introduced in a feature selection phase to facilitate the segregation of behaviors. These reweighted features are further processed by biological operations of crossover and mutation to adapt varying signal patterns for significant accuracy results. Experiments on wearable sensors benchmark datasets HMP, WISDM and self-annotated IMSB datasets have been demonstrated to testify the efficacy of the proposed work over state-of-the-art methods.
TL;DR: Experimental results show that the strategy and parameter self-adaptive mechanisms can improve the performance of the evolutionary algorithms, and that SPS-PSO can achieve higher classification accuracy and obtain more concise solutions than those of the other algorithms on the large-scale feature problems selected in this research.
TL;DR: The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems.
Abstract: At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering.
TL;DR: The proposed DGHNL model with a 29-layer structure is capable to achieve the prediction accuracy of 94.60% for the Statlog German credit approval data, which is the best prediction performance for this well-known credit scoring dataset, compared to the existing work in the field.
TL;DR: A Modified Binary GWO based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance and shows the superiority of the proposed algorithm compared to binary versions of the-state-of-the-art optimization techniques.
Abstract: Grey Wolf Optimizer (GWO) simulates the grey wolves’ nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon’s rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.
TL;DR: A generalized surrogate-assisted evolutionary algorithm based on the optimization framework of the genetic algorithm (GA) based on a surrogate-based trust region local search method, a surrogates-guided GA (SGA) updating mechanism with a neighbor region partition strategy and a prescreening strategybased on the expected improvement infilling criterion of a simplified Kriging in the optimization process.
Abstract: Engineering optimization problems usually involve computationally expensive simulations and many design variables. Solving such problems in an efficient manner is still a major challenge. In this paper, a generalized surrogate-assisted evolutionary algorithm is proposed to solve such high-dimensional expensive problems. The proposed algorithm is based on the optimization framework of the genetic algorithm (GA). This algorithm proposes to use a surrogate-based trust region local search method, a surrogate-guided GA (SGA) updating mechanism with a neighbor region partition strategy and a prescreening strategy based on the expected improvement infilling criterion of a simplified Kriging in the optimization process. The SGA updating mechanism is a special characteristic of the proposed algorithm. This mechanism makes a fusion between surrogates and the evolutionary algorithm. The neighbor region partition strategy effectively retains the diversity of the population. Moreover, multiple surrogates used in the SGA updating mechanism make the proposed algorithm optimize robustly. The proposed algorithm is validated by testing several high-dimensional numerical benchmark problems with dimensions varying from 30 to 100, and an overall comparison is made between the proposed algorithm and other optimization algorithms. The results show that the proposed algorithm is very efficient and promising for optimizing high-dimensional expensive problems.
TL;DR: A comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems.
Abstract: In this article, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.
TL;DR: A cost-minimized synchronization-oriented location routing model that minimizes the total generalized cost, which includes internal transportation cost, value of eco-packages, short-term benefits and environmental externalities is proposed.
Abstract: Optimization of the green logistics location-routing problem with eco-packages involves solving a two-echelon location-routing problem and the pickup and delivery problem with time windows. The first echelon consists of large eco-package transport, which is modeled by a time-discretized transport-concentrated network flow programming in the resource sharing state–space–time (SST) network. The second echelon focuses on small eco-package pickups and deliveries, established by the cost-minimized synchronization-oriented location routing model that minimizes the total generalized cost, which includes internal transportation cost, value of eco-packages, short-term benefits and environmental externalities. In addition, the Gaussian mixture clustering algorithm is utilized to assign customers to their respective service providers in the pickup and delivery process, and a Clarke–Wright saving method-based non-dominated sorting genetic algorithm II is designed to optimize pickup and delivery routes, and improve their cost-effectiveness and degree of synchronization. Different strategy testing results are used in the service phase as input data to calculate the cost of the transport phase, which is solved through a Lagrangian relaxation approach. The 3D SST network representation innovatively captures the eco-package route sequence and state transition constraints over the shortest path in the pickup and delivery at any given moment of the transport phase. A large-scale logistics network in Chengdu, China, is used to demonstrate the proposed model and algorithm, and undertake sensitivity analysis considering the life cycle of green eco-packages.
TL;DR: Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.
01 Jan 2020
TL;DR: This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm, which mimics Darwinian theory of survival of the fittest in nature.
Abstract: Genetic Algorithm (GA) is one of the most well-regarded evolutionary algorithms in the history. This algorithm mimics Darwinian theory of survival of the fittest in nature. This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm. The most popular improvements in the main component of this algorithm (selection, crossover, and mutation) are given too. The chapter also investigates the application of this technique in the field of image processing. In fact, the GA algorithm is employed to reconstruct a binary image from a completely random image.